distilbert-base-cased-finetuned-paper

reyhanemyr/distilbert-base-cased-finetuned-paper

distilbert-base-cased-finetuned-paper is a token classification model from reyhanemyr. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

Downloads · 30 days

2% of all-time downloads

All-time downloads

326

Public

Repo size

1.3 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin261 MB · 100%

At a glance

Task: Token Classification
Library: transformers
License: apache-2.0
Model type: distilbert
Access: Public
Created: Mar 13, 2023
Updated: Mar 13, 2023
SHA: 96b22f73

Try a prompt

Model card

From the Hugging Face model README

distilbert-base-cased-finetuned-paper

This model is a fine-tuned version of distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1417
Precision: 0.5690
Recall: 0.6226
F1: 0.5946
Accuracy: 0.9790

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	62	0.1183	0.3810	0.3019	0.3368	0.9702
No log	2.0	124	0.0923	0.3986	0.5189	0.4508	0.9730
No log	3.0	186	0.0808	0.5	0.5943	0.5431	0.9765
No log	4.0	248	0.0951	0.6042	0.5472	0.5743	0.9790
No log	5.0	310	0.0939	0.5794	0.5849	0.5822	0.9795
No log	6.0	372	0.0949	0.5508	0.6132	0.5804	0.9792
No log	7.0	434	0.1004	0.5075	0.6415	0.5667	0.9782
No log	8.0	496	0.1110	0.5403	0.6321	0.5826	0.9784
0.0979	9.0	558	0.1139	0.5517	0.6038	0.5766	0.9789
0.0979	10.0	620	0.1153	0.5727	0.5943	0.5833	0.9795
0.0979	11.0	682	0.1238	0.5238	0.6226	0.5690	0.9776
0.0979	12.0	744	0.1249	0.5478	0.5943	0.5701	0.9786
0.0979	13.0	806	0.1263	0.5323	0.6226	0.5739	0.9786
0.0979	14.0	868	0.1303	0.5810	0.5755	0.5782	0.9792
0.0979	15.0	930	0.1358	0.4929	0.6509	0.5610	0.9773
0.0979	16.0	992	0.1305	0.5766	0.6038	0.5899	0.9793
0.0033	17.0	1054	0.1321	0.5323	0.6226	0.5739	0.9779
0.0033	18.0	1116	0.1353	0.5726	0.6321	0.6009	0.9789
0.0033	19.0	1178	0.1355	0.5462	0.6132	0.5778	0.9793
0.0033	20.0	1240	0.1346	0.5556	0.6132	0.5830	0.9796
0.0033	21.0	1302	0.1386	0.5403	0.6321	0.5826	0.9784
0.0033	22.0	1364	0.1389	0.5508	0.6132	0.5804	0.9790
0.0033	23.0	1426	0.1376	0.55	0.6226	0.5841	0.9790
0.0033	24.0	1488	0.1394	0.5641	0.6226	0.5919	0.9796
0.0012	25.0	1550	0.1408	0.55	0.6226	0.5841	0.9789
0.0012	26.0	1612	0.1413	0.5739	0.6226	0.5973	0.9792
0.0012	27.0	1674	0.1417	0.5455	0.6226	0.5815	0.9787
0.0012	28.0	1736	0.1424	0.5455	0.6226	0.5815	0.9787
0.0012	29.0	1798	0.1419	0.5546	0.6226	0.5867	0.9790
0.0012	30.0	1860	0.1417	0.5690	0.6226	0.5946	0.9790

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2

Task: Token Classification
Library: transformers
Type: distilbert
License: apache-2.0
Created: Mar 13, 2023
Updated: Mar 13, 2023